Engineer - Data Engineering in Chennai, Tennessee at CBTS

CBTS serves enterprise and midmarket clients in all industries across the United States and Canada. CBTS combines deep technical expertise with a full suite of flexible technology solutions--including Application Modernization, Managed Hybrid Cloud, Cybersecurity, Unified Communications, and Infrastructure solutions. From developing and deploying modern applications and the secure, scalable platforms on which they run, to managing, monitoring, and optimizing their operations, CBTS delivers comprehensive technology solutions for its clients' transformative business initiatives. For more information, please visit www.cbts.com.

OnX is a leading technology solution provider that serves businesses, healthcare organizations, and government agencies across Canada. OnX combines deep technical expertise with a full suite of flexible technology solutions—including Generative AI, Application Modernization, Managed Hybrid Cloud, Cybersecurity, Unified Communications, and Infrastructure solutions. From developing and deploying modern applications and the secure, scalable platforms on which they run, to managing, monitoring, and optimizing their operations, OnX delivers comprehensive technology solutions for its clients’ transformative business initiatives. For more information, please visit www.onx.com.

ENGINEER - DATA ENGINEERING - JOB DESCRIPTION

JOB TITLE: Engineer - Data Engineering

Role: Engineer

Role Variant: Data Engineering

1. Role Purpose (1–3 lines): Executes core data engineering development work across ingestion, transformation, modeling & integration pipelines to enable high quality trusted data availability for business analytics, AI/ML, and product systems.

2. Key Responsibilities:

· Build, test and maintain ETL/ELT pipelines, ingestion frameworks and data integration connectors across batch / streaming.

· Implement data transformations, quality rules, profiling checks, data cleansing routines and schema enforcement.

· Develop data models, curated layers, staging layers and support semantic readiness for analytical consumption.

· Support deployment, CI/CD, code repository hygiene, job scheduling, monitoring and run support.

· Follow standards for naming, folder structure, pipeline modularity, code performance and engineering quality.

3. Key Performance Indicators (KPIs):

· On time delivery of data engineering modules

· Pipeline reliability / failure rate reduction

· Data quality rule pass %

· Reusable component adoption

· Engineering coding & performance hygiene score
4. Qualifications & Experience:

· Degree: Graduate / Professional Degree in Engineering, preferably in Computer Science, Data Science, AI/ML Engineering, Mathematics, Statistics.

· Years of experience: Graduate – 4 to 6 years

· Relevant experience: 2 + years

7. Key Interfaces:

· Internal: Collaborates with Data Engineering, Cloud Platform, Enterprise Apps, Product Owners, Business Domain Teams and Security teams for data pipeline alignment, data quality readiness, schema enforcement and consumption enablement.

· External: Coordinates with cloud hyperscalers, SI partners, data platform OEM vendors, support engineering teams and enterprise customers for integration enablement, platform compatibility assurance and deployment support.